Frequent Subgraph Mining (FSM) is a well-known operation on graphs, and plays an important role in many graph-based applications, such as indexing, classification, and social network analysis. Most existing efforts in mining frequent subgraphs target graphs that do not change over time. However, recent practical applications utilize graphs that are continuously being updated.
Emerging graph-based applications, however, are now required to manage substantially continuously changing graphs, such as social networks and web graphs. Social network graphs, for example, with the frequent addition and removal of users, as well as the evolving relationships among users, exhibit rapid changes in size and structure. Thus, an efficient solution for mining such graphs is important for these applications.
A need therefore exists for improved frequent subgraph mining techniques that support efficient frequent subgraph mining on dynamic graphs by maintaining a reduced amount of information relative to conventional techniques, namely, the graph's embeddings that are collected during the incremental computations.